Spaces:
Sleeping
Sleeping
File size: 2,594 Bytes
3ef0f76 49b68ea 3efa7c6 3ef0f76 c9b9539 bfc50b8 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 |
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
import pickle
import pandas as pd
# Instantiate The Fast API instance
app = FastAPI(
title="Sepsis Prediction API",
description="This FastAPI application provides sepsis predictions using a machine learning model.",
version="1.0"
)
# Load the model and key components
with open('model_and_key_components.pkl', 'rb') as file:
loaded_components = pickle.load(file)
loaded_model = loaded_components['model']
loaded_encoder = loaded_components['encoder']
loaded_scaler = loaded_components['scaler']
# Define the input data structure using Pydantic BaseModel
class InputData(BaseModel):
PRG: int
PL: float
PR: float
SK: float
TS: int
M11: float
BD2: float
Age: int
# Define the output data structure using Pydantic BaseModel
class OutputData(BaseModel):
Sepsis: str
# Define a function to preprocess input data
def preprocess_input_data(input_data: InputData):
# Encode Categorical Variables (if needed)
# All columns are numerical. No need for encoding
# Apply scaling to numerical data
numerical_cols = ['PRG', 'PL', 'PR', 'SK', 'TS', 'M11', 'BD2', 'Age']
input_data_scaled = loaded_scaler.transform([list(input_data.dict().values())])
return pd.DataFrame(input_data_scaled, columns=numerical_cols)
# Define a function to make predictions
def make_predictions(input_data_scaled_df: pd.DataFrame):
y_pred = loaded_model.predict(input_data_scaled_df)
sepsis_mapping = {0: 'Negative', 1: 'Positive'}
return sepsis_mapping[y_pred[0]]
@app.get("/")
async def root():
# Endpoint at the root URL ("/") returns a welcome message with a clickable link
message = "Welcome to the Sepsis Classification API! This API Provides predictions for Sepsis based on several medical inputs. To use this API, please access the API documentation here: https://rasmodev-sepsis-prediction.hf.space/docs/"
return message
@app.post("/predict/", response_model=OutputData)
async def predict_sepsis(input_data: InputData):
try:
input_data_scaled_df = preprocess_input_data(input_data)
sepsis_status = make_predictions(input_data_scaled_df)
return {"Sepsis": sepsis_status}
except Exception as e:
# Handle exceptions and return an error response
raise HTTPException(status_code=500, detail=str(e))
if __name__ == "__main__":
import uvicorn
# Run the FastAPI application on the local host and port 7860
CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860", "--reload"] |